The invention discloses a visual loopback detection method based on an auto-encoding network. The visual loopback detection method comprises the following steps: 1, acquiring an image; 2, calculatinga memorability score of the image, comparing the memorability score with a set memorability score threshold value, determining whether to reserve the image or not, and determining a key frame; 3, inputting the screened key frames into a trained convolutional self-encoding network, and obtaining a GIST global feature f after noise reduction; 4, taking out a feature fpre from the feature database, calculating cosine similarity of two feature vectors fpre and f, comparing the cosine similarity with a set similarity threshold, determining whether the frame is a candidate frame or not, and performing loop-back verification; and 5, in a loopback verification stage, on the premise of completing space consistency verification, carrying out time consistency verification, enabling one image to meetloopback conditions and become loopback candidate frames in a continuous motion process, enabling the obtained key frames to become candidate frames within a certain time range, and finally determining loopback only when the conditions are met.